Measurement properties of the Pain Self-Efficacy Questionnaire in populations with musculoskeletal disorders: a systematic review
Bibliographic record
Abstract
Abstract A higher level of pain self-efficacy has been suggested as a predictor of a better outcome in patients with musculoskeletal disorders. The Pain Self-Efficacy Questionnaire (PSEQ) is one of the most frequently used patient-reported outcome measures for pain self-efficacy. The purpose of this study was to conduct a systematic review that would identify, appraise, and synthetize the psychometric properties of the PSEQ. Embase, MEDLINE, and CINAHL databases were searched for publications reporting on psychometric properties of the PSEQ in populations with musculoskeletal disorders. After applying selection criteria on identified citations, 28 studies (9853 participants) were included. The methodological quality as measured with the COSMIN risk of bias tool varied from adequate to very good for most measurement properties. The results showed a weighted mean intraclass correlation coefficient of 0.86 (range: 0.75–0.93) for test–retest reliability for the original 10-item PSEQ and the minimal detectable change at 95% confidence interval was 11.52 out of 60 points. Effect size and standardized response mean values were 0.53 and 0.63, respectively, whereas the minimal clinically important difference ranged from 5.5 to 8.5 in patients with chronic low back pain. Internal consistency (Cronbach alpha) ranged from 0.79 to 0.95. The results also showed that the PSEQ has low to moderate correlations with measures of quality of life, disability, pain, pain interference, anxiety, depression, and catastrophizing. Finally, the PSEQ has been adapted and validated in 14 languages. Overall, the results demonstrate that the PSEQ has excellent validity, reliability, and responsiveness. Further high-quality studies are needed to determine responsiveness in populations other than chronic low back pain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".